Systems and methods for identifying distracted driving events using unsupervised clustering

Abstract

A distracted driving analysis system for identifying distracted driving events is provided. The system includes a processor in communication with a memory device programmed to: (i) receive driving event records including phone usage by a user that occurred within a time period of a driving event, (ii) divide the driving event records into at least two clusters based at least in part upon common features of one or more driving event records of the plurality of driving event records by processing the driving event records using an unsupervised machine learning algorithm, (iii) generate a trained model based at least in part upon the at least two clusters including cluster labels, (iv) process a new driving event using the trained model, (v) assign the new driving event to one of the at least two clusters using the trained model, and (vi) based at least in part upon the cluster labels for the assigned cluster, determine whether the new driving event is an actual distracted driving event or a passenger event.

I claim: 
     
       1. A distracted driving analysis system for identifying distracted driving events, the distracted driving analysis system comprising at least one processor in communication with at least one memory device, wherein the at least one processor is programmed to:
 receive a plurality of driving event records, each driving event record including phone usage by a user that occurred within a time period of a driving event associated with the user; 
 divide the plurality of driving event records into at least two clusters based upon common features of each driving event record by processing the plurality of driving event records using an unsupervised machine learning algorithm; 
 generate a trained model based upon the at least two clusters including cluster labels; 
 process a new driving event using the trained model; 
 assign the new driving event to one of the at least two clusters using the trained model; and 
 based upon a cluster labels for the assigned cluster, determine whether the new driving event is an actual distracted driving event or a passenger event. 
 
     
     
       2. The system of  claim 1 , wherein the at least one processor is further programmed to:
 receive feature inputs from a user computer device, wherein the feature inputs indicate the common features of the driving event records that should be analyzed in order to categorize and label the driving event records; 
 display information related to the at least two clusters to a user through the user computer device; and 
 receive the cluster labels from the user computer device, the cluster labels indicating whether the driving event records in each cluster represent actual distracted driving events or passenger events. 
 
     
     
       3. The system of  claim 1 , wherein the at least one processor is further programmed to:
 receive a new driving event associated with a second user; 
 determine, using the trained model, whether the new driving event is an actual distracted driving event or a passenger event; and 
 assign a category to the new driving event as an actual distracted driving event or a passenger event. 
 
     
     
       4. The system of  claim 3 , wherein the at least one processor is further programmed to assign a confidence level to the category assigned to the new driving event. 
     
     
       5. The system of  claim 4 , wherein the at least one processor is further programmed to generate a driver profile for the second user, wherein the driver profile includes the categorized new driving event. 
     
     
       6. The system of  claim 5 , wherein the at least one processor is further programmed to:
 calculate a user safety score based at least in part upon the categorized new driving event; and 
 include the user safety score in the driver profile. 
 
     
     
       7. The system of  claim 6 , wherein the at least one processor is further programmed to generate an insurance policy based at least in part upon the driver profile. 
     
     
       8. The system of  claim 1 , wherein the at least one processor is further configured to:
 receive historical driving data including a plurality of driving trips associated with at least one user and historical phone usage data including a plurality of instances of phone usage associated with at least one user; 
 analyze the historical driving data and historical phone usage data; and 
 determine a plurality of instances of phone usage by a user that occurred within the time period of a driving trip associated with the user. 
 
     
     
       9. A computer-implemented method for identifying distracted driving events using a distracted driving analysis system including at least one processor in communication with at least one memory device, the method comprising:
 receiving a plurality of driving event records, each driving event record including phone usage by a user that occurred within a time period of a driving event associated with the user; 
 dividing the plurality of driving event records into at least two clusters based upon common features of each driving event record by processing the plurality of driving event records using an unsupervised machine learning algorithm; 
 generating a trained model based upon the at least two clusters including cluster labels; 
 processing a new driving event using the trained model; 
 assigning the new driving event to one of the at least two clusters using the trained model; and 
 based upon a cluster labels for the assigned cluster, determining whether the new driving event is an actual distracted driving event or a passenger event. 
 
     
     
       10. The computer-implemented method of  claim 9  further comprising:
 receiving feature inputs from a user computer device, wherein the feature inputs indicate the common features of the driving event records that should be analyzed in order to categorize and label the driving event records; 
 displaying information related to the at least two clusters to a user through the user computer device; and 
 receiving the cluster labels from the user computer device, the cluster labels indicating whether the driving event records in each cluster represent actual distracted driving events or passenger events. 
 
     
     
       11. The computer-implemented method of  claim 9  further comprising:
 receiving a new driving event associated with a second user; 
 determining, using the trained model, whether the new driving event is an actual distracted driving event or a passenger event; and 
 assigning a category to the new driving event as an actual distracted driving event or a passenger event. 
 
     
     
       12. The computer-implemented method of  claim 11  further comprising assigning a confidence level to the category assigned to the new driving event. 
     
     
       13. The computer-implemented method of  claim 12  further comprising generating a driver profile for the second user, wherein the driver profile includes the categorized new driving event. 
     
     
       14. The computer-implemented method of  claim 13  further comprising:
 calculating a user safety score based at least in part upon the categorized new driving event; and 
 including the user safety score in the driver profile. 
 
     
     
       15. The computer-implemented method of  claim 14  further comprising generating an insurance policy based at least in part upon the driver profile. 
     
     
       16. A non-transitory computer-readable storage medium having computer-executable instructions embodied thereon, wherein when executed by a distracted driving analysis system including at least one processor in communication with at least one memory device, the computer-executable instructions cause the at least one processor to:
 receive a plurality of driving event records, each driving event record including phone usage by a user that occurred within a time period of a driving event associated with the user; 
 divide the plurality of driving event records into at least two clusters based upon common features of each driving event record by processing the plurality of driving event records using an unsupervised machine learning algorithm; 
 generate a trained model based upon the at least two clusters including cluster labels; 
 process a new driving event using the trained model; 
 assign the new driving event to one of the at least two clusters using the trained model; and 
 based upon a cluster labels for the assigned cluster, determine whether the new driving event is an actual distracted driving event or a passenger event. 
 
     
     
       17. The non-transitory computer-readable storage medium of  claim 16 , wherein the computer-executable instructions further cause the at least one processor to:
 receive feature inputs from a user computer device, wherein the feature inputs indicate the common features of the driving event records that should be analyzed in order to categorize and label the driving event records; 
 display information related to the at least two clusters to a user through the user computer device; and 
 receive the cluster labels from the user computer device, the cluster labels indicating whether the driving event records in each cluster represent actual distracted driving events or passenger events. 
 
     
     
       18. The non-transitory computer-readable storage medium of  claim 16 , wherein the computer-executable instructions further cause the at least one processor to:
 receive a new driving event associated with a second user; 
 determine, using the trained model, whether the new driving event is an actual distracted driving event or a passenger event; and 
 assign a category to the new driving event as an actual distracted driving event or a passenger event. 
 
     
     
       19. The non-transitory computer-readable storage medium of  claim 18 , wherein the computer-executable instructions further cause the at least one processor to assign a confidence level to the category assigned to the new driving event. 
     
     
       20. The non-transitory computer-readable storage medium of  claim 19 , wherein the computer-executable instructions further cause the at least one processor to generate a driver profile for the second user, wherein the driver profile includes the categorized new driving event.